docs: def quic_graph_lasso_ebic_manual(X, gamma=0):
      """Run QuicGraphicalLasso with mode='path' and gamma; use EBIC criteria for model
      selection.
  
      The EBIC criteria is built into InverseCovarianceEstimator base class
      so we demonstrate those utilities here.
      """
      print("QuicGraphicalLasso (manual EBIC) with:")
      print("   mode: path")
      print("   gamma: {}".format(gamma))
      model = QuicGraphicalLasso(
          lam=1.0,
          mode="path",
          init_method="cov",
          path=np.logspace(np.log10(0.01), np.log10(1.0), num=100, endpoint=True),
      )
      model.fit(X)
      ebic_index = model.ebic_select(gamma=gamma)
      covariance_ = model.covariance_[ebic_index]
      precision_ = model.precision_[ebic_index]
      lam_ = model.lam_at_index(ebic_index)
      print("   len(path lams): {}".format(len(model.path_)))
      print("   lam_scale_: {}".format(model.lam_scale_))
      print("   lam_: {}".format(lam_))
      print("   ebic_index: {}".format(ebic_index))
      return covariance_, precision_, lam_